Genomics-based individualized care, often referred to as personalized medicine (PM), is undergoing a revolution. Soon sequencing of an individual patient's entire genome will be feasible in routine clinical care. Patients will be confronted with the possibility of receiving tens or even hundreds of genomic results that have important clinical implications - from treatment response to disease risk to implications for family members. Given the wide scope of possible results from PM, developing and evaluating evidence to support the use of this information to guide individualized care will be challenging. We propose to address these challenges by conducting a broad range of health economics based research activities. The overall goal of this project is to move the field of PM forward in an efficient and appropriate manner by developing novel approaches to assess the value of PM and prioritize PM research. We will organize our approach using our previously developed Expected Value of Individualized Care (EVIC) conceptual framework. One of the crucial assumptions in EVIC computations is that individualized care is perfectly implemented when corresponding evidence or tests are available. As we have learned from the adoption of the genomic tests to date, this does not hold true in practice. In the proposed work, we will extend the EVIC framework to fundamental aspects of decision-making at the patient, physician, and payer levels to capture implementations rates and the complexity of PM in the whole genome sequencing era. We will illustrate how such an expanded EVIC model can provide a common basis for prioritizing research investments in developing new genomic tests and generating evidence for existing tests by private and public investors. We will assess population, provider, and payer preferences, including personal utility and willingness to pay, by conducting national surveys. Data from these surveys will be used to refine the EVIC framework. Lastly, we will develop a pragmatic framework to address evidence uncertainty in the development of clinical guideline and reimbursement policies for existing PM applications. We will accomplish this aim by assessing the value of conducting future research on PM case studies identified in collaboration with policymakers, including guidelines groups and payers. Based on these case studies, we will use a consensus-based approach to develop a pragmatic framework to help decision makers assess 'insufficient' vs. 'sufficient' evidence for making a recommendation. In summary, this research project will provide: 1) an encompassing approach to assess optimal research opportunities in PM, 2) a better understanding of the value of PM, including personal utility, and 3) a more consistent approach for developing PM clinical guideline recommendations and reimbursement policies.